Determining a likelihood of a directional transition at a junction in an encoded routability graph description

ABSTRACT

Techniques are provided, which may be implemented in various methods, apparatuses, and/or articles of manufacture, to obtain an encoded routability graph representative of feasible paths in an indoor environment represented by an encoded map, and assign likelihoods of transition from an ingress edge in the encoded routability graph to individual egress edges through a junction connecting the ingress edge to a plurality of egress edges based, at least in part, on one or more features of the encoded map.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

This application claims priority under 35 USC 119 to U.S. ProvisionalApplication Ser. No. 61/648,165, filed Oct. 17, 2011, and entitled,“DETERMINING A LIKELIHOOD OF DIRECTIONAL TRANSITIONS FOR AN ENCODEDROUTEABILITY GRAPH DESCRIPTION”, which is assigned to the assigneehereof and which is incorporated herein by reference.

BACKGROUND

1. Field

The subject matter disclosed herein relates to electronic devices, andmore particularly to methods, apparatuses and articles of manufacturefor use in determining one or more likelihoods of transition for anencoded routability graph description for use with an encoded mappertaining to at least a portion of a structure.

2. Information

The Global Positioning System (GPS) represents one type of GlobalNavigation Satellite System (GNSS), which along with other types ofsatellite positioning systems (SPS) provide or otherwise supportsignal-based position location capabilities (e.g., positioningfunctions) in mobile devices, and particularly in outdoor environments.However, since some satellite signals may not be reliably receivedand/or acquired by a mobile device within an indoor environment or otherlike mixed indoor/outdoor environments, different techniques may beemployed to enable position location services.

For example, mobile devices may attempt to obtain a position fix bymeasuring ranges to three or more terrestrial transmitters (e.g.,wireless access points, beacons, cell towers, etc.) which are positionedat known locations. Such ranges may be measured, for example, byobtaining a MAC ID address from signals received from such transmittersand obtaining range measurements to the transmitters by measuring one ormore characteristics of signals received from such transmitters such as,for example, signal strength, a round trip delay time, etc.

These and other like position location and/or navigation techniques tendto be of further benefit to a user if presented with certain mappedfeatures. For example, mapped features may relate to or otherwiseidentify certain physical objects, characteristics, or points ofinterest within a building or complex, etc. Thus, in certain instances,an indoor positioning/navigation system may provide an encoded map tomobile device upon entering a particular indoor area. Such a map mayshow indoor features such as doors, hallways, entry ways, walls, etc.,points of interest such as bathrooms, pay phones, room names, stores,etc. Such an encoded map may be stored at a server to be accessible by amobile device through selection of a URL, for example. By obtaining anddisplaying such a map, a mobile device may overlay a current location ofthe mobile device (and user) over the displayed map to provide the userwith additional context.

Furthermore, by generating or otherwise obtaining an encoded routabilitygraph relating to an encoded map, a positioning engine or other likecapability of a mobile device may be used to navigate within an indoorstructure. However, in certain instances an encoded routability graphmay be fairly large and/or otherwise computationally complex and hencemay burden some mobile devices. Hence there is a continuing desire toreduce size and/or complexity of such encoded files and/or reduce aburden of processing such files.

SUMMARY

In accordance with one aspect, a method may be provided which comprises,with a computing platform: obtaining an encoded routability graphrepresentative of feasible paths in an indoor environment represented byan encoded map; and assigning likelihoods of transition from an ingressedge in the encoded routability graph to individual egress edges througha junction connecting the ingress edge to a plurality of egress edgesbased, at least in part, on one or more features of the encoded map.

In accordance with another aspect, an apparatus for use in a computingplatform may be provided which comprises: means for obtaining an encodedroutability graph representative of feasible paths in an indoorenvironment represented by an encoded map; and means for assigninglikelihoods of transition from an ingress edge in the encodedroutability graph to individual egress edges through a junctionconnecting the ingress edge to a plurality of egress edges based, atleast in part, on one or more features of the encoded map.

In accordance with yet another aspect, a computing platform may beprovided which comprises: memory; and one or more processing units to:obtain, from the memory, an encoded routability graph representative offeasible paths in an indoor environment represented by an encoded map;and assign likelihoods of transition from an ingress edge in the encodedroutability graph to individual egress edges through a junctionconnecting the ingress edge to a plurality of egress edges based, atleast in part, on one or more features of the encoded map.

In accordance with still another aspect, in article of manufacture foruse in a computing platform may be provided which comprises: anon-transitory computer-readable medium having stored therein computerimplementable instructions executable by one or more processing unitsto: obtain an encoded routability graph representative of feasible pathsin an indoor environment represented by an encoded map; and assignlikelihoods of transition from an ingress edge in the encodedroutability graph to individual egress edges through a junctionconnecting the ingress edge to a plurality of egress edges based, atleast in part, on one or more features of the encoded map.

BRIEF DESCRIPTION OF DRAWINGS

Non-limiting and non-exhaustive aspects are described with reference tothe following figures, wherein like reference numerals refer to likeparts throughout the various figures unless otherwise specified.

FIG. 1 is a schematic block diagram illustrating a computing andcommunication environment, in which one or more likelihoods oftransition for an encoded routability graph description may begenerated, transmitted, stored, and/or otherwise processed, inaccordance with an example implementation.

FIG. 2 is a schematic block diagram illustrating certain features of acomputing device that may generate one or more likelihoods of transitionfor an encoded routability graph description, in accordance with anexample implementation.

FIG. 3 is a schematic block diagram illustrating certain features of amobile device that may generate or otherwise obtain one or morelikelihoods of transition for an encoded routability graph description,in accordance with an example implementation.

FIG. 4 is an illustrative diagram showing a reduced set of junctions ina diagram of a floor of a v-shaped office, in accordance with an exampleimplementation.

FIG. 5 is an illustrative diagram showing two junctions in the form ofnodes that are connected together and a plurality of ingress and egressedges, along with a plurality of possible paths corresponding thereto,in accordance with an example implementation.

FIG. 6 is a flow diagram illustrating an example method that may beimplemented in whole or part in with a computing device and/or with amobile device to generate one or more likelihoods of transition for anencoded routability graph description, in accordance with animplementation.

DETAILED DESCRIPTION

Various techniques are described herein which may be implemented in oneor more computing platforms to generate one or more likelihoods oftransition for an encoded routability graph description. In certainexample instances, generating one or more likelihoods of transition foran encoded routability graph description in advance may reduce aprocessing burden of a mobile device, and/or possibly improveperformance of a mobile device while it is being used to assist a userin navigating an indoor structure.

The use of the term “encoded” herein with respect to various data filesand/or instructions files that may be processed in support of thetechniques provided herein. An encoded data and/or instruction file may,for example, comprise a plurality of logic values or bits that via oneor more coding schemes may be indicative of one or more objects,actions, capabilities, features, and/or the like or some combinationthereof relating to some subject. For example, in certain instances, anencoded map may be generated to represent certain modeled and/or realworld objects and/or other like features and/or areas of a structurethat may appear in a map of a structure and which may be interest to orother affect movement of a user within such a structure. In anotherexample, an encoded routability graph may be generated based, at leastin part, on an encoded map to represent certain junctions andinterconnecting edges which may relate to certain feasible movements ofa user within a particular structure. In certain examples, one or moreencoded data and/or instruction files may be further processed toprovide data compression, encryption, error-correction, and/or the likeor some combination thereof.

An encoded routability graph description may, for example, relate to anencoded map of an indoor structure and indicate paths that a person mayfollow within the indoor structure. For example, an encoded routabilitygraph description may indicate one or more paths leading from an entryway of an indoor space to one or more rooms or other spaces and/orobjects therein. Here, for example, a path may be indicated based on aplurality of interconnected junctions that a person may navigatebetween. Such a path may, for example, pass through one or morehallways/corridors, one or more doorways, one or more staircases, and/orother like features that may be indicated or otherwise determined froman electronic map (e.g., floor plan, office layout, etc.). However, forexample, such a path may not pass through a solid wall, floor, ceiling,desk, and/or some other like object or feature that a person could notor would not usually pass through.

While a simple indoor structure may only comprise one or possibly just afew feasible paths that a person may follow, it should be recognizedthat certain (e.g., larger or more complex) indoor structures mayprovide a plethora of feasible paths that a person may follow. With thisin mind, as described in greater detail herein, in certain instances itmay be beneficial to reduce the number of feasible paths that a personmay follow. For example, in certain instances, a reduced version of anencoded routability graph may be employed which may significantly reducethe number of interconnected junctions in a grid or other like patternof nodes. For example, with an encoded routability graph, a reducedversion of an encoded routability graph, and/or the like or somecombination thereof, it may be further beneficial to determine certainpossible paths that a person may follow from among the feasible paths,e.g., based, at least in part, shortest or other likely routes relatingto one or more particular features of the indoor structure, one or moreparticular points of interest, and/or the like or some combinationthereof. Hence, in certain instances, a set of possible paths mayrepresent all or part of a set of feasible paths. Hence, in certaininstances, a possible path may transit through a plurality of feasiblepaths. In other words, a possible path may comprise a string of feasiblepaths, e.g., extending from one point of interest to another point ofinterest.

As expected, some paths may pass through the same hallway or doorwaybefore splitting off in different directions. Consequently, a person maychange a direction of travel (following one of the paths) at or nearby aparticular junction. For example, at one end of a hallway there may betwo rooms located on opposite sides of the hallway. Accordingly, ajunction may be represented at or nearby the end of the hallway, andwhich may be connected by edges to respective junctions located in thetwo rooms. Thus, a user at or nearby the junction in the hallway mayfollow a particular path along one of the edges into one of the rooms(e.g., by turning left or right at the end of the hallway). In certaininstances, a user may also turn-around or otherwise reverse theirdirection at such a junction to retrace a previous path. In thisexample, assuming that a person does not terminate their path of travelat or nearby the junction at the end of the hallway, then there arethree possible directional transitions, namely, left, right, orturn-back.

It should be recognized that other (non-terminating) junctions mayprovide for fewer or greater number of possible direction transitions.For example, a simple non-terminating junction may be connected betweentwo other junctions by two edges. Hence, depending on the path, one ofthe edges may represent an ingress edge and the other may represent anegress edge with respect to that particular junction. For example, onepath may follow an ingress edge towards such a particular junction, andan egress edge away from the particular junction. If the person decidesto turn-around at or nearby such a particular junction, then such areversed path may follow an ingress edge towards the particularjunction, and the same edge away from the particular junction, in whichcase the ingress edge has become the egress edge. Thus, a junction may,for example, comprise one or more ingress edges and one or more egressedges, and a (feasible and/or possible) path may follow one or moreedges between two junctions.

Thus, as used herein, an edge represents a path that is consideredfeasible for a user of a mobile device to follow within an indoorenvironment of a structure from one location corresponding to onejunction in an encoded routability graph to another locationcorresponding to another junction in the encoded routability graph.Hence, depending on a user's direction of travel, an edge may take theform of an ingress edge with respect to a particular junction when itleads towards that particular junction. Conversely, depending on auser's direction of travel, an edge may take the form of an egress edgewith respect to a particular junction with it leads away from thatparticular junction. Of course, in certain instances (e.g. with the useris turning around or reversing their course) and edge a take an initialform of an egress edge only to become an ingress edge.

If two junctions in an encoded routability graph are not connected by anedge, then the lack of such an edge represents a path that is consideredinfeasible for a user of a mobile device to follow within an indoorenvironment of the structure associated with the encoded routabilitygraph. Thus, for example, since a user of a mobile device will likely beunable to follow a path through an obstacle such as, e.g., a wall, anassociated encoded routability graph would not include an edge betweentwo junctions located on opposite sides of such an obstacle.

As pointed out in greater detail herein, it may be useful to assignlikelihoods of transition for one or more edges associated with one ormore junctions. For example, it may be useful for a positioning enginein a mobile device to consider one or more values (e.g., directionalprobabilities, weights, etc.) relating to such likelihoods of transitionto estimate a direction of travel of a person (user) carrying orotherwise moving with the mobile device. Hence, for example, a particlefilter, Kalman filter, and/or the like of a positioning engine may takeinto account a probability value or other like metric that a usertraveling from one junction to another junction along an ingress edgemay or may not transition to a particular egress edge in continuing ontheir path of travel.

In accordance with certain aspects, one or more likelihoods oftransition may, for example, be determined in advance (a priori) based,at least in part, on one or more features of an encoded map. Moreover,all or part of such likelihoods of transition may, for example, bedetermined by one or more computing platforms located internal and/orexternal to a mobile device.

For example, as described in greater detail herein, a computing platformmay obtain an encoded routability graph that is representative of atleast a portion of the feasible paths in an indoor environmentrepresented by an encoded map. The computing platform may, for example,assign likelihoods of transition from an ingress edge in the encodedroutability graph to individual egress edges through a junctionconnecting the ingress edge to the egress edges based, at least in part,on one or more features of the encoded map.

In certain further example implementations, a computing platform mayassign likelihoods of transition based, at least in part, on possibleorigination paths in the encoded routability graph connected to thejunction through the ingress edge and possible destination pathsconnected to the junction through the egress edge. For example, acomputing platform may determine at least one likelihood of transitionfrom the ingress edge to one of the egress edges based, at least inpart, on a ratio of a number of possible paths through the egress edgeleading away from the junction to a number of possible paths through theingress edge leading toward the junction.

In certain example implementations, possible origination paths and/orpossible destination paths may be based, at least in part, onpoint-to-point connections between nodes in a reduced version of anencoded routability graph. For example, in certain instances a node in areduced version of an encoded routability graph may be assigned a weightvalue that is based, at least in part, on a number of encodedconnectivity graph nodes represented by the node. For example, incertain instances a node in a reduced version of an encoded routabilitygraph indicative of macro scale feasible paths, may comprise/consume orotherwise represent a plurality of nodes of an encoded connectivitygraph which may indicate relatively smaller micro scale feasible paths.

In certain example implementations, possible origination paths and/orpossible destination paths may be based, at least in part, onpoint-to-point connections between nodes representing points of interestin an indoor environment represented by an encoded map along the encodedroutability graph. In certain example instances, a node representing atleast one point of interest may be assigned a weight value relative toat least one other node representing at least another point of interest.In certain example implementations, a computing platform may determineat least one possible origination path in the encoded routability graphusing an all-pairs shortest path (APSP) algorithm.

In certain example implementations, a computing platform may affect atleast one weight value assigned to at least one node in an encodedroutability graph corresponding to at least one junction.

In certain example implementations, a likelihood of transition may berepresentative of a possible turn-back transition at a junction in whichan ingress edge further represents an egress edge. In certain instances,a likelihood of transition that is representative of a possibleturn-back transition may, for example, comprise a predetermined value(e.g., a nominal value, a null value, etc.).

FIG. 1 is a schematic block diagram illustrating a computing andcommunication environment 100, in which one or more values representingone or more likelihoods of transition 116 for an encoded routabilitygraph description may be generated, transmitted, stored, and/orotherwise processed, in accordance with an example implementation.

Example environment 100 may comprise a computing device 102 having anapparatus 104 for use in generating all or part of likelihoods oftransition 116. As described in greater detail below, apparatus 104 may,for example, obtain an encoded routability graph description for anencoded map (e.g., one or more electronic files relating to a floorplan, office layout, CAD drawing, etc.) for a structure that includes anindoor area that may be navigated in some manner by a user of a mobiledevice 106. Encoded map may, for example, indicate various features thatmay affect a user's travel within the structure in some manner. Forexample, certain features may indicate various types of routeobstructions present within an indoor area. For example certain featuresmay indicate regions of space that may or may not be traveled by a user.For example, certain features may indicate certain locations, objects,services, etc., that may be of interest at times to one or more users.

An encoded routability graph description may, for example, indicate aset of grid points (e.g., nodes, junctions) that may be assigned to at aportion of an encoded map of an indoor area and which may beinterconnected to represent feasible paths that a user may travel. Incertain instances, an encoded routability graph description may, forexample, indicate a reduced version of an encoded routability graph inwhich certain grid points have been selectively merged to reduce the setof grid points. Thus, for example, in certain implementations, anencoded routability graph description may comprise selectively mergedgrid points in which certain neighboring grid points may have absorbedother grid points in light of an absence of route obstructions betweenthe grid points locations. There are a variety of ways in which a set ofinterconnected grid points maybe reduced. Accordingly, all of part of afull set and/or some reduced set of grid points may be identified as anencoded routability graph at apparatus 104 for use in generating all orpart of likelihoods of transition 116 as described in herein.

As shown, computing device 102 may be connected to one or more wiredand/or wireless network(s) 110 via a communication link 112. A mobiledevice 106 may, for example, be coupled to network(s) 110 via acommunication link 114. As such, in certain example implementations, oneor more values representing likelihoods of transition 116 may betransmitted or otherwise provided by computing device 102 to mobiledevice 106. For example, all or part of likelihoods of transition 116may represent an a priori directional probability value that may be usedby a positioning engine in a mobile device to estimate a likely path oftravel, etc.

In certain example implementations, mobile device 106 may, for example,comprise an apparatus 108 that may itself generate all or part of all orpart of the values representing likelihoods of transition 116. Asillustrated herein, one or more computing platforms may be provided inone or more devices to generate all or part of all or part of the valuesrepresenting likelihoods of transition 116. Apparatus 108 may further oralternatively obtain, store, transmit, and/or otherwise process all orpart of the values representing likelihoods of transition 116.

Network(s) 110 may, for example, be further connected to one or moreother resources (devices) 120, e.g., via communication link 118. Incertain example implementations, computing device 102 and/or mobiledevice 106 may obtain, from other resources (devices) 120, all or partof one or more encoded maps and/or other like diagrams, and/or one ormore encoded routability graphs and/or other like data files, etc.,which may be of use in generating or otherwise processing all or part ofall or part of the values representing likelihoods of transition 116 fora structure.

In certain example implementations, environment 100 may further compriseone or more transmitting devices 130 which may transmit one or moresignals 132 that may be received by a mobile device 106 and used, atleast in part, to support signal-based positioning capabilities and/orother like navigation capabilities. By way of example, one or moretransmitting devices 130 may be part of, or otherwise support, aSatellite Positioning System (SPS) such as a Global Navigation SatelliteSystem (GNSS), regional positioning/navigation system, etc. In certainexamples, one or more transmitting devices 130 may be part of, orotherwise support, a terrestrial-based Location Based Service (LBS)and/or the like, which may be implemented via one or more cellularcommunication networks, one or more wireless communication networks, oneor more dedicated beacon transmitting devices, and/or the like or somecombination thereof. In certain instances one or more transmittingdevices 130 may provide additional communication services to mobiledevice 106, it may therefore be coupled to and/or part of network(s)110. For example one or more transmitting devices 130 may compriseaccess points within a wireless local area network, etc., e.g. asrepresented in FIG. one by the dashed connecting line betweentransmitting device(s) 130 and network(s) 110. Consequently, in certaininstances, signal 132 may comprise bidirectional wireless communicationsbetween a wireless transmitting device 130 and mobile device 106.

As may be appreciated, in certain instances one or more signals 132 fromone or more transmitting devices 130 may be acquired by mobile device106 and used to estimate its position location in some manner. Whilemobile device 106 is within a structure, in certain instances some ofthe signals and 32 from some of transmitting devices 130 may not beacquired due to interference and/or other signal propagation factorspresented by the various objects that make up the structure, and/orobjects within the structure. Hence, for example, in certain structuresa mobile device 106 may be unable to adequately acquire SPS signalstransmitted by orbiting satellites. However, in certain instances, whilewithin a structure a mobile device 106 may be able to adequately acquireterrestrial-based transmissions that may augment SPS signals, and/orrepresent signals associated with a location based service and/or thelike. Thus for example, while inside certain structures, mobile device106 may attempt to estimate its position location based on one or moresignals 132 obtained from one or more transmitting devices 130, and incertain instances one or more of such transmitting devices 130 may bearranged within the structure and/or about the structure so as toprovide adequate coverage within the structure. Such techniques andsystems are well known and gaining in popularity.

The term “structure” may, for example, apply to (all or part of) one ormore natural and/or man-made physical arrangements of object(s), theknowledge of which may be of use to a user of mobile device 106. Forexample, a structure may comprise one or more buildings or a portionthereof. A “feature” may, for example, identify an object or obstacle(e.g., a wall, a door, an elevator, a staircase, a statue, etc.), anentity and/or service (e.g., a business, a taxi stand, a restroom, adoctor's office, a lost and found department, a security station, etc.),and/or any other positioning/navigational, location based servicecharacteristic which may be identified via representative data in one ormore files of an electronic map. In certain instances, for example, afeature may represent something to navigate around (e.g., such as aroute obstacle), navigate towards or through (e.g., a doorway, anelevator, etc.), or possibly navigate away from (e.g., a staircase,). Ofcourse these are simply a few examples and, as with all of the examplespresented herein, claimed subject matter is not necessarily intended tobe so limited.

Reference is made next to FIG. 2, which is a schematic block diagramillustrating certain features of a computing device 102, for example asin FIG. 1, in form of a computing platform 200 that may generate one ormore values representing one or more likelihoods of transition for anencoded routability graph description, in accordance with an exampleimplementation.

As illustrated computing platform may comprise one or more processingunits 202 to perform data processing (e.g., in accordance with thetechniques provided herein) coupled to memory 204 via one or moreconnections 206. Processing unit(s) 202 may, for example, be implementedin hardware or a combination of hardware and software. Processingunit(s) 202 may, for example, be representative of one or more circuitsconfigurable to perform at least a portion of a data computing procedureor process. By way of example but not limitation, a processing unit mayinclude one or more processors, controllers, microprocessors,microcontrollers, application specific integrated circuits, digitalsignal processors, programmable logic devices, field programmable gatearrays, and the like, or any combination thereof.

Memory 204 may be representative of any data storage mechanism. Memory204 may include, for example, a primary memory 204-1 and/or a secondarymemory 204-2. Primary memory 204-1 may comprise, for example, a randomaccess memory, read only memory, etc. While illustrated in this exampleas being separate from the processing units, it should be understoodthat all or part of a primary memory may be provided within or otherwiseco-located/coupled with processing unit(s) 202, or other like circuitrywithin computing device 102. Secondary memory 204-2 may comprise, forexample, the same or similar type of memory as primary memory and/or oneor more data storage devices or systems, such as, for example, a diskdrive, an optical disc drive, a tape drive, a solid state memory drive,etc. In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, a (non-transitory)computer readable medium 250. Memory 204 and/or computer readable medium250 may comprise computer-implementable instructions 252 for certainexample techniques as provided herein.

As illustrated in FIG. 2, at various times, memory 204 may store certainsignals representing data and/or computer-implementable instructions forcertain example techniques as provided herein. For example, memory 204may store data and/or computer-implementable instructions for apparatus104. By way of example, memory 204 may at various times storerepresentative data for one or more values representing one or morelikelihoods of transition 116, one or more encoded maps 210 or portionsthereof, all or part of an encoded routability graph description 212,one or more feasible and/or possible paths 214, one or more edges 216,one or more junctions 218, one or more features 220, one or more pointsof interest 222, an all-pairs shortest path (APSP) algorithm 224 and/orthe like, one or more ratios 226, one or more weight values 228, and/orthe like or some combination thereof.

As shown, computing device 102 may, for example, comprise a networkinterface 208. Network interface 208 may, for example, provide acapability to receive and/or transmit wired and/or wireless signals,e.g., to communicate via network(s) 110 (FIG. 1).

In certain example implementations, computing platform 200 may take theform of a server or other like device. In certain exampleimplementations, computing platform 200 may take the form of a wirelessnetwork element, or other location based service element. In certainexample implementations, computing platform 200 may take the form of aportion of a cloud computing configuration. In certain exampleimplementations, computing platform 200 may take the form of a wirelessaccess point or other like local area network computing resource.

Reference is made next to FIG. 3, which is a schematic block diagramillustrating certain features of a mobile device 106, e.g., as in FIG.1, in the form of a computing platform 300 that may generate orotherwise obtain one or more values representing one or more likelihoodsof transition for an encoded routability graph description, inaccordance with an example implementation.

As illustrated, computing platform 300 may comprise one or moreprocessing units 302 to perform data processing (e.g., in accordancewith the techniques provided herein) coupled to memory 304 via one ormore connections 306. Processing unit(s) 302 may, for example, beimplemented in hardware or a combination of hardware and software.Processing unit(s) 302 may, for example, be representative of one ormore circuits configurable to perform at least a portion of a datacomputing procedure or process. By way of example but not limitation, aprocessing unit may include one or more processors, controllers,microprocessors, microcontrollers, application specific integratedcircuits, digital signal processors, programmable logic devices, fieldprogrammable gate arrays, and the like, or any combination thereof.

Memory 304 may be representative of any data storage mechanism. Memory304 may include, for example, a primary memory 304-1 and/or a secondarymemory 304-2. Primary memory 304-1 may comprise, for example, a randomaccess memory, read only memory, etc. While illustrated in this exampleas being separate from the processing units, it should be understoodthat all or part of a primary memory may be provided within or otherwiseco-located/coupled with processing unit(s) 302, or other like circuitrywithin mobile device 106. Secondary memory 304-2 may comprise, forexample, the same or similar type of memory as primary memory and/or oneor more data storage devices or systems, such as, for example, a diskdrive, an optical disc drive, a tape drive, a solid state memory drive,etc. In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, a (non-transitory)computer readable medium 320. Memory 304 and/or computer readable medium320 may comprise computer-implementable instructions 322 for certainexample techniques as provided herein.

As illustrated in FIG. 3, at various times, memory 304 may store certainsignals representing data and/or computer-implementable instructions forcertain example techniques as provided herein. For example, memory 304may store data and/or computer-implementable instructions for apparatus108. By way of example, memory 304 may at various times storerepresentative data for one or more values representing one or morelikelihoods of transition 116, one or more encoded maps 210 or portionsthereof, all or part of an encoded routability graph description 212,one or more feasible and/or possible paths 214, one or more edges 216,one or more junctions 218, one or more features 220, one or more pointsof interest 222, an all-pairs shortest path (APSP) algorithm 224 and/orthe like, one or more ratios 226, one or more weight values 228, apositioning engine 314, one or more estimated destinations 316, and/orthe like or some combination thereof.

As shown, mobile device 106 may, for example, comprise a wirelessinterface 308. Wireless interface 308 may, for example, provide acapability to receive and/or transmit wired and/or wireless signals,e.g., to communicate via network(s) 110, and/or one or more transmitterdevices 130 (FIG. 1). Wireless interface 308 may be comprised of one ormore interfaces possibly including but not limited to interfaces forwide area networks (WAN) such as GSM, UMTS, CDMA, LTE, WCDMA and CDMA2000 and interfaces for personal area networks (PAN) such as WiFi andBluetooth. It is also understood that there may be multiple wirelessinterfaces 308 that may be used simultaneously or individually. Wirelessinterface 308, may in certain implementations also concurrently and/oralternatively act as a receiver device (and/or transceiver device) toacquire signals 132 (FIG. 1) from one or more transmitting devices 130for use in position location and/or other positioning/navigationservices, e.g. that may be supported by terrestrial locating services,such as e.g., one or more LBS which may be provided, at least in part,by a cellular network, a WiFi network, etc. In certain exampleimplementations, wireless interface 308 may also be representative ofone or more wired network interfaces.

As shown, mobile device 106 may, for example, may comprise a SPSreceiver 310, which may provide position location and/or othernavigation services based on certain signals 132 transmitted by one ormore transmitting devices 130. For example, SPS receiver 310 maycomprise an SPS receiver capable of receiving and processing one or moreGNSS, or other like satellite and/or terrestrial location systems. TheSPS receiver 310 may be used for various purposes such as forpositioning/navigation of the mobile device and for location basedservices (LBS), such as, e.g., one or more LBS which may be provided, atleast in part, by a cellular network, a WiFi network, etc. SPS receiver310 may operatively provide location information and/or otherwiseoperate in some manner with positioning engine 314. In certaininstances, all or part of positioning engine 314 may be provided by SPSreceiver 310.

As shown, mobile device 106 may comprise one or more user interfaces312. For example user interface 312 may be representative of one or moreuser input and/or user output devices. Thus, for example, user interface312 may comprise a keypad, a touch screen, various buttons, variousindicators, a display screen, a speaker, a microphone, a projector, acamera, etc.

Mobile device 106 is representative of any electronic device that may bemoved about within environment 100. For example, mobile device 106 maycomprise a hand-held computing and/or communication device, such as, amobile telephone, a smart phone, a lap top computer, a tablet computer,a positioning/navigation device, and/or the like. In certain exampleimplementations, mobile device 106 may be part of a circuit board, anelectronic chip, etc.

It should be understood that mobile device 106 may also or alternativelycomprise one or more other circuits, mechanisms, etc., (not shown) thatmay be of use in performing one or more other functions or capabilities,and/or supportive of certain example techniques as provided herein.

Computing device 102 and/or mobile device 106 may, for example, beenabled (e.g., via one or more network interfaces 208, one or morewireless interfaces 308, etc.) for use with various wirelesscommunication networks such as a wireless wide area network (WWAN), awireless local area network (WLAN), a wireless personal area network(WPAN), and so on. The term “network” and “system” may be usedinterchangeably herein. A WWAN may be a Code Division Multiple Access(CDMA) network, a Time Division Multiple Access (TDMA) network, aFrequency Division Multiple Access (FDMA) network, an OrthogonalFrequency Division Multiple Access (OFDMA) network, a Single-CarrierFrequency Division Multiple Access (SC-FDMA) network, and so on. A CDMAnetwork may implement one or more radio access technologies (RATs) suchas cdma2000, Wideband-CDMA (W-CDMA), Time Division Synchronous CodeDivision Multiple Access (TD-SCDMA), to name just a few radiotechnologies. Here, cdma2000 may include technologies implementedaccording to IS-95, IS-2000, and IS-856 standards. A TDMA network mayimplement Global System for Mobile Communications (GSM), DigitalAdvanced Mobile Phone System (D-AMPS), or some other RAT. GSM and W-CDMAare described in documents from a consortium named “3rd GenerationPartnership Project” (3GPP). Cdma2000 is described in documents from aconsortium named “3rd Generation Partnership Project 2” (3GPP2). 3GPPand 3GPP2 documents are publicly available. A WLAN may include an IEEE802.11x network, and a WPAN may include a Bluetooth network, an IEEE802.15x, for example. Wireless communication networks may includeso-called next generation technologies (e.g., “4G”), such as, forexample, Long Term Evolution (LTE), Advanced LTE, WiMAX, Ultra MobileBroadband (UMB), and/or the like.

An encoded map of an indoor space may, for example, be derived from aCAD or other like drawing or file. Thus, a CAD drawing may show roomsand hallways formed by walls, doorways, etc., and/or other objects, orpoints of interest. Once the CAD drawing is transformed to a particularformat usable as a digital map, the digital map may be electronicallystored for access by various computing platforms as discussed above.

In one particular implementation, an encoded routability graph maycomprise or may be based on a set of grid of points which may bevirtually projected over an area covered by a map of an indoor area suchas a floor of an office building, shopping mall, school building, etc.Neighboring (adjacent) grid points may then be selectively connected byedges subject to features in the map to indicate possible directtransitions between locations of the neighboring points withoutobstruction (e.g., walls). Thus, for example, interconnected grid pointsmay form “nodes” in an encoded routability graph for use in modelingmovement of a mobile device in the indoor area. One or more of the nodesmay, for example, represent a junction or be represented by a junction.

In particular implementations, a location of a mobile device may bemodeled as being placed at points along edges connecting neighboringnodes in the encoded routability graph described above. Likewise,transitions from an initial position to a subsequent position may bemodeled to occur along edges of the encoded routability graph. Inaddition, a likelihood model may further characterize possibletransitions of a mobile device from an initial position to a subsequentposition over a time period. In a particular example, a Kalman orparticle filtering model and/or the like may establish a likelihood thata mobile device have a particular subsequent location, velocity andheading that is conditioned on an initial location, velocity andheading.

To provide sufficient granularity in certain example encoded routabilitygraph descriptions, grid points may be projected over an indoor areadensely so that an encoded routability graph may be sufficientlygranular to support particular applications. However, determining anencoded routability graph and/or transition probabilities for a densepopulation of grid points may be computationally intensive. Likewise,executing mobile applications to perform routing, particle filtering,etc., processing resources of a mobile device may be taxed bycomputing/evaluating detailed routes and/or a large number/high densityof particles using particle filtering techniques. As such, it may beuseful to reduce the number of nodes or junctions in an encodedroutability graph, e.g., to provide sufficient granularity in encodedroutability graphs and/or density of particles without over taxingprocessing resources. Thus, in certain example instances, possible pathsmay be determined using a reduced encoded routability graph description.

Reference is made next to FIG. 4, which is an illustrative diagramshowing a reduced set of junctions in a diagram of a floor plan of av-shaped office building 400, which depicts various features as may befound in encoded map. For example, building 400 comprises a plurality ofoffice spaces interconnected via hallways, etc.

An encoded routability graph is further illustrated in FIG. 4 by aplurality of nodes 402 that are dispersed about the floor plan. Forexample, a node 402-1 and a node 402-2 are shown as being interconnectedby an edge 404. As can be seen, some nodes (e.g., node 402-3) have oneedge which renders them terminating nodes, while other nodes (e.g., node402-1) have two or more edges which renders them non-terminating nodes.

A path of travel through or within building 400 may, for example,generally follow some subset of the point-to-point connectionsillustrated by applicable nodes and edges. As may be appreciated,certain nodes or junctions may be related (e.g., co-located, adjacent,nearby, etc.) to one or more points of interest. For example, a breakroom or meeting room, or possibly bathroom, may be of interest to theuser and hence represents a point of interest. Further, buildingentrances and/or exits may also represent points of interest. Hence,some possible paths may lead to one or more particular points ofinterest. It should be noted, that the encoded routability graphillustrated in the example in FIG. 4 represents a reduced version of anencoded routability graph, in accordance with an implementation.Clearly, other encoded routability graphs may be used; for example, anencoded routability graph may include additional nodes or junctionsaligned with other grid points, etc. Further, it should be noted thatusers typically take shorter routes to certain points of interest orother destinations. Of course, some users may get lost or run intodetours which cause them to take longer paths. As described herein a setof feasible paths may be reduced to a set of possible paths in somemanner, e.g., considering points of interest, considering shortestpaths, etc. Hence, if the user does happen to take the shortest path,then the determined likelihoods of transition may prove useful in apositioning engine that may be supporting our tracking user movements.

FIG. 5 is an illustrative diagram showing a portion 500 of an encodedroutability graph comprising two junctions (labeled x and y) in the formof nodes that are connected together and a plurality of ingress andegress edges, along with a plurality of possible paths correspondingthereto, in accordance with an example implementation.

As shown, junctions y and x are connected by an edge labeled xy. In thisexample, several possible paths are shown which follow edge xy fromjunction y to junction x, and one path is shown which does not followedge xy. In this example, junction y has two edges, y1 and y2, which actas ingress edges with regard to junction y and paths P1 and P2, andpaths P4 and P5, respectively. Junction x, in this example, has threeedges, labeled x1, x2 and x3, each of which represents an egress edgewith respect to at least one of the example paths. For example, path P1follows ingress edge y1 to junction y, edge xy to junction x, and exitsvia egress edge x1. Example path P2 follows ingress edge y1 to junctiony, edge xy to junction x, and exits via egress edge x2. Path P4, forexample, follows ingress edge y2 two junction y, edge xy to junction x,and exits via egress edge x1. Example path P5 follows ingress edge y1 tojunction y, edge xy to junction x, and exits via egress edge x3. Examplepath P3 follows edge x2 (as an ingress edge) to junction x, and exitsjunction x via egress edge x3. Hence, example path P3 does not followedge xy.

As mentioned in certain implementations, possible paths flowing throughan edge to a destination may be identified or determined based upon anidentification of point-to-point connections of nodes in a reducedversion of an encoded routability graph flowing through the edge. Incertain other implementations, possible paths flowing through an edgemay be identified or determined based upon point-to-point connection ofpoints of interests (POIs) such as specific rooms or areas in an indoorenvironment represented by an encoded map.

It should be understood that given multiple choices of direction from ajunction and the various possible paths that a user may follow, it isunlikely that each of the directions will have the same likelihood(e.g., probability value) of transition. Accordingly, analysis ofencoded map features may determine a priori probabilities for eachdirection. Such probabilities may, for example, depend on thedestinations in each direction. It should be kept in mind, however,while such directional probabilities may not be applicable at a microscale (e.g., an encoded connectivity graph) scale, they may beapplicable at a macro scale (e.g., an encoded routability graph, reducedversion of an encoded routability graph, etc.) in which possible pathsto one or more destination are part of the macro scale graph.

As pointed out herein, directional probabilities may be used to providehints to a positioning engine about an expected next-macro-hopdestination for a user. For example, at a mobile device having apositioning engine that uses a particle filter and/or the like, mayoperate more efficiently with a priori probability values of where themobile device is likely to be headed at a macro scale.

In certain example implementations, one may let a flow designate a pathfrom one node to another, and provide each node with a weight that maycomprise flows that start at that specific node and flows that passthrough that specific node. Thus, for example, each node will haveincoming flows from all its neighbors and flows that start at theparticular node. Such flows may be split between the edges to itsneighbor nodes and which terminate at the particular node itself.Accordingly, a ratio in which the flows are split may correspond to adirectional probability value of paths with regard to the egress edgesfrom the particular node.

In this example, let: XY represent a set of all paths from nodes of anencoded routability graph flowing through junction x; X1 represent asubset of paths in xy flowing through junction x to egress edge x1; X2represent a subset of paths in xy flowing through junction x to egressedge x2; and, X3 represent a subset of paths in xy flowing throughjunction x to egress edge x3. Note that path P3, as previouslymentioned, does not flow through xy.

Accordingly, as described in further detail below, one or more valuesrepresenting one or more likelihoods of transition from ingress edge xythrough junction x to egress edges x1, x2 and x3 may be estimated asfollows (e.g., in the form of a probability value):

P _(xy→x1) =N(X1)/N(XY);

P _(xy→x2) =N(X2)/N(XY); and

P _(xy→x3) =N(X3)/N(XY),

where N(ξ) represents the cardinality of (or count of elements in) setξ.

Thus, for example, given that the user travels from junction y tojunction x, one may calculate a probability that the user will departjunction x on egress edges x1, x2, and x3. Such calculation may, forexample, result in one or more values representing a likelihood oftransition for each of these edges. For example in portion 500 of FIG.5, there are four flows (P1, P2, P4, P5) on edge xy, and two of thoseflows (P1, P4) continue on egress edge x1, one of those flows (P2)continues on egress edge x2, and one of those flows (P5) continues onegress edge x3. Hence, for example, a probability that a user willdepart junction x on egress edge x1 may be 50% because two flows out ofthe four flows continue on egress edge x1; a probability that a userwill depart junction x on egress edge x2 may be 25% because one flow outof the four flows continues on egress edge x2; and a probability that auser will depart junction x on egress edge x3 may be 25% because oneflow out of the four flows continues on egress edge x3. Again, suchexample probabilities may be determined based on a ratio of a number ofpossible paths through the egress edge leading away from the junction toa number of possible paths through the ingress edge leading toward thejunction. As previously mentioned, in certain instances a valuerepresenting a likelihood of transition may, for example, be added to,or otherwise used in some manner to affect a change in, a weight thatmay be assigned to a node or junction. Each path (e.g. P1, P2, P3) mayalso have a weight representing how often the path itself is used. Theseweights may be used, for example, to calculate the transitionprobabilities as described above.

Additionally, in certain implementations it may be beneficial to includea value representing a likelihood of transition for a user who decidesto turn around or reverse their direction travel at or near a junction.Thus for example, in certain instances a nominal value (e.g.,predetermined, dynamically determined, etc.) may be used to representsuch a likelihood. Thus, in the previous example it may be beneficial toassign a turnaround probability of 5%, which may slightly reduce theother probabilities accordingly.

In certain example implementations, various methods may be used todetermine possible paths with regard to an encoded routability graph.For example, in certain implementations an APSP algorithm and/or thelike may be run from each node on a reduced version of an encodedroutability graph. After each APSP, and for each edge xy in the SP onemay, for example: increment a counter xy; increment one of the countersy1, y2, e.g., depending on a source of a path; and increment one of thecounters x1, x2, x3, e.g., depending on a destination of a path. E

In certain example implementations, it may be that each node or junctionin the reduced version of an encoded routability graph may have acorresponding weight w that represents a number of an encodedconnectivity graph grid points or nodes absorbed into it and/or which itotherwise represents. Here for example, the counters may be incrementedor otherwise affected in some manner based, at least in part, on aweight w of the node or junction.

Attention is drawn next to FIG. 6, which is a flow diagram illustratingan example process or method that may be implemented in whole or part inone or more computing platforms to generate one or more valuesrepresenting one or more likelihoods of transition for an encodedroutability graph description, in accordance with an implementation.

At example block 602, an encoded routability graph description may beobtained. For example, an encoded routability graph description may beobtained from one or more other devices 120 (FIG. 1), and/or otherwisedetermined using known techniques. In certain instances, an encodedroutability graph description may comprise a reduced version of anencoded routability graph. An encoded routability graph description may,for example, relate to all or part of an encoded map for all or part ofa structure comprising an indoor space. As previously mentioned, andencoded routability graph description may specify a plurality ofjunctions which are interconnected in some manner by edges.Additionally, one or more of the junctions may correspond to one or moreparticular features, points of interest, etc. that may be included in orotherwise derived from an encoded routability graph description. As usedherein, for simplicity the phrase “encoded routability graphdescription” may simply be referred to as an encoded routability graphand/or a reduced version of an encoded routability graph. Also, theterms node and junction may be used interchangeably.

At example block 604, one or more values representing one or morelikelihoods of transition may be assigned to individual egress edgesfrom a junction based, at least in part, on one or more features of anencoded map. In certain example implementations, possible paths in theencoded routability graph may be determined, e.g. at block 606. At block608, one or more values representing one or more likelihoods oftransition may be assigned based, at least in part, on possibleorigination paths and/or possible destination paths. At block 610, oneor more values representing one or more likelihoods of transition may bebased, at least in part, on a ratio of a number of possible pathsleading away from a junction and a number of possible paths leadingtowards a junction.

At example block 612, in certain instances one or more valuesrepresenting one or more likelihoods of transition may be transmitted orotherwise provided to one or more mobile devices. Here, for example,computing device 102 may transmit one or more values representing one ormore likelihoods of transition, as shown in block 612, to mobile device106, via network(s) 110 (FIG. 1).

At example block 614, a mobile device may use one or more valuesrepresenting one or more likelihoods of transition in one or morefunctions. For example, a mobile device may use one or more valuesrepresenting one or more likelihoods of transition in a positioningengine or other like positioning or navigation capability. For example,in certain implementations a positioning engine may comprise a particlefilter and/or the like which may use one or more values representing oneor more likelihoods of transition to possibly help predict or otherwiseestimate a direction of travel and/or potential destination of a userwith regard to a possible path, an encoded routability graph, a point ofinterest, and/or other feature(s) of an encoded map.

It should be recognized from the examples provided herein that a mobiledevice having knowledge of one or more a priori directional probabilityvalues may improve performance. Further, as pointed out in some of theexamples herein users may not simply move from node to node in anencoded routability graph but rather users tend to move on paths fromand/or to certain points of interest and/or other like features that maybe identifiable in advance from an encoded map and/or other like encodedfiles. By considering the shortest paths between such features and/orpoints of interests using an encoded routability graph, one maydetermine a likelihood of transition and provide such information to apositioning engine for consideration when estimating a direction oftravel and/or some destination. Thus, for example, one may define a setof points of interests and/or various classes of points of interest andtheir locations in an encoded routability graph, and consider thepossible (e.g., shortest) paths on such routability graph which lead toor from one or more points of interest. For example, if possible pathmay lead from an office to an exit or from a meeting room to a bathroom,etc. As mentioned, in certain instances it may be beneficial to assign aweight value to such nodes or junctions, and/or edges, which may thenrepresent, or be used to determine, a value representing a likelihood oftransition. For example, a weight value may be used to calculate adirectional probability value, e.g., as previously shown. If otherweights are used in an encoded routability graph then in certaininstances, it may be possible to combine the weight values of thepresent techniques with other weights to further estimate a likelihoodof transition for a given note or junction. For example, if a node orjunction has a weight w that is indicative of a number of nodes or gridpoints that it represents, then it may be possible and beneficial tocombine or otherwise take into account the weight w with a correspondingweight value relating to a likelihood of transition calculation.

Reference throughout this specification to “one example”, “an example”,“certain examples”, or “example implementation” means that a particularfeature, structure, or characteristic described in connection with thefeature and/or example may be included in at least one feature and/orexample of claimed subject matter. Thus, the appearances of the phrase“in one example”, “an example”, “in certain examples” or “in certainimplementations” or other like phrases in various places throughout thisspecification are not necessarily all referring to the same feature,example, and/or limitation. Furthermore, the particular features,structures, or characteristics may be combined in one or more examplesand/or features.

The methodologies described herein may be implemented by various meansdepending upon applications according to particular features and/orexamples. For example, such methodologies may be implemented inhardware, firmware, and/or combinations thereof, along with software. Ina hardware implementation, for example, a processing unit may beimplemented within one or more application specific integrated circuits(ASICs), digital signal processors (DSPs), digital signal processingdevices (DSPDs), programmable logic devices (PLDs), field programmablegate arrays (FPGAs), processors, controllers, micro-controllers,microprocessors, electronic devices, other devices units designed toperform the functions described herein, and/or combinations thereof.

In the preceding detailed description, numerous specific details havebeen set forth to provide a thorough understanding of claimed subjectmatter. However, it will be understood by those skilled in the art thatclaimed subject matter may be practiced without these specific details.In other instances, methods and apparatuses that would be known by oneof ordinary skill have not been described in detail so as not to obscureclaimed subject matter.

Some portions of the preceding detailed description have been presentedin terms of algorithms or symbolic representations of operations onbinary digital electronic signals stored within a memory of a specificapparatus or special purpose computing device or platform. In thecontext of this particular specification, the term specific apparatus orthe like includes a general purpose computer once it is programmed toperform particular functions pursuant to instructions from programsoftware. Algorithmic descriptions or symbolic representations areexamples of techniques used by those of ordinary skill in the signalprocessing or related arts to convey the substance of their work toothers skilled in the art. An algorithm is here, and generally, isconsidered to be a self-consistent sequence of operations or similarsignal processing leading to a desired result. In this context,operations or processing involve physical manipulation of physicalquantities. Typically, although not necessarily, such quantities maytake the form of electrical or magnetic signals capable of being stored,transferred, combined, compared or otherwise manipulated as electronicsignals representing information (e.g., as representative data). It hasproven convenient at times, principally for reasons of common usage, torefer to such signals as bits, data, values, elements, symbols,characters, terms, numbers, numerals, information, or the like. Itshould be understood, however, that all of these or similar terms are tobe associated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, as apparentfrom the following discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining”, “establishing”, “obtaining”,“identifying”, and/or the like refer to actions or processes of aspecific apparatus, such as a special purpose computer or a similarspecial purpose electronic computing device. In the context of thisspecification, therefore, a special purpose computer or a similarspecial purpose electronic computing device is capable of manipulatingor transforming signals, typically represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of the specialpurpose computer or similar special purpose electronic computing device.In the context of this particular patent application, the term “specificapparatus” may include a general purpose computer once it is programmedto perform particular functions pursuant to instructions from programsoftware.

The terms, “and”, “or”, and “and/or” as used herein may include avariety of meanings that also are expected to depend at least in partupon the context in which such terms are used. Typically, “or” if usedto associate a list, such as A, B or C, is intended to mean A, B, and C,here used in the inclusive sense, as well as A, B or C, here used in theexclusive sense. In addition, the term “one or more” as used herein maybe used to describe any feature, structure, or characteristic in thesingular or may be used to describe a plurality or some othercombination of features, structures or characteristics. Though, itshould be noted that this is merely an illustrative example and claimedsubject matter is not limited to this example.

While there has been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein.

Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within the scope of appendedclaims, and equivalents thereof.

What is claimed is:
 1. A method comprising, with a computing platform:obtaining an encoded routability graph representative of feasible pathsin an indoor environment represented by an encoded map; and assigninglikelihoods of transition from an ingress edge in said encodedroutability graph to individual egress edges through a junctionconnecting said ingress edge to a plurality of egress edges based, atleast in part, on one or more features of said encoded map.
 2. Themethod of claim 1, wherein assigning said likelihoods of transitionfurther comprises: assigning said likelihoods of transition based, atleast in part, on possible origination paths in said encoded routabilitygraph connected to said junction through said ingress edge and possibledestination paths connected to said junction through said individualegress edges.
 3. The method of claim 2, and further comprising, withsaid computing platform: determining said likelihoods of transitionbased, at least in part, on a ratio of a number of said possibledestination paths through said individual egress edges leading away fromsaid junction to a number of said possible origination paths throughsaid ingress edge leading toward said junction.
 4. The method of claim2, wherein said possible origination paths and said possible destinationpaths are based, at least in part, on point-to-point connections betweennodes in a reduced version of said encoded routability graph.
 5. Themethod of claim 4, wherein at least one of said nodes in said reducedversion of said encoded routability graph is assigned a weight valuethat is based, at least in part, on a number of an encoded connectivitygraph nodes represented by said at least one of said nodes.
 6. Themethod of claim 2, wherein said possible origination paths and saidpossible destination paths are based, at least in part, onpoint-to-point connections between nodes representing points of interestin said encoded map along said encoded routability graph.
 7. The methodof claim 6, wherein at least one of said nodes representing at least oneof said points of interest is assigned a weight value relative to atleast one other node representing at least another one of said points ofinterest.
 8. The method of claim 2, and further comprising, with saidcomputing platform: affecting at least one weight value assigned to atleast one node in said encoded routability graph corresponding to saidjunction.
 9. The method of claim 2, wherein at least one of saidlikelihoods of transition is representative of a possible turn-backtransition at said junction in which said ingress edge furtherrepresents one of said plurality of egress edges.
 10. The method ofclaim 9, wherein said at least one of said likelihoods of transitionthat is representative of said possible turn-back transition comprises apredetermined value.
 11. The method of claim 2, and further comprising,with said computing platform: determining at least one of said possibleorigination paths in said encoded routability graph using an all-pairsshortest path (APSP) algorithm.
 12. The method of claim 1, wherein atleast one of said likelihoods of transition comprises an a prioridirection probability value.
 13. The method of claim 12, wherein said apriori direction probability value is configured for use by apositioning engine in a mobile device to estimate a potentialdestination regarding a disposition of said mobile device with regard tosaid encoded routability graph.
 14. The method of claim 1, and furthercomprising, with said computing platform: providing at least one of saidlikelihoods of transition to at least one mobile device.
 15. The methodof claim 1, wherein said computing platform is provided within a mobiledevice.
 16. An apparatus for use in a computing platform, the apparatuscomprising: means for obtaining an encoded routability graphrepresentative of feasible paths in an indoor environment represented byan encoded map; and means for assigning likelihoods of transition froman ingress edge in said encoded routability graph to individual egressedges through a junction connecting said ingress edge to a plurality ofegress edges based, at least in part, on one or more features of saidencoded map.
 17. The apparatus of claim 16, and further comprising:means for assigning said likelihoods of transition based, at least inpart, on possible origination paths in said encoded routability graphconnected to said junction through said ingress edge and possibledestination paths connected to said junction through said individualegress edges.
 18. The apparatus of claim 17, and further comprising:means for determining said likelihoods of transition based, at least inpart, on a ratio of a number of said possible destination paths throughsaid individual egress edges leading away from said junction to a numberof said possible origination paths through said ingress edge leadingtoward said junction.
 19. The apparatus of claim 17, wherein saidpossible origination paths and said possible destination paths arebased, at least in part, on point-to-point connections between nodes ina reduced version of said encoded routability graph.
 20. The apparatusof claim 19, wherein at least one of said nodes in said reduced versionof said encoded routability graph is assigned a weight value that isbased, at least in part, on a number of an encoded connectivity graphnodes represented by said at least one of said nodes.
 21. The apparatusof claim 17, wherein said possible origination paths and said possibledestination paths are based, at least in part, on point-to-pointconnections between nodes representing points of interest in saidencoded map along said encoded routability graph.
 22. The apparatus ofclaim 21, wherein at least one of said nodes representing at least oneof said points of interest is assigned a weight value relative to atleast one other node representing at least another one of said points ofinterest.
 23. The apparatus of claim 17, and further comprising: meansfor affecting at least one weight value assigned to at least one node insaid encoded routability graph corresponding to said junction.
 24. Theapparatus of claim 17, wherein at least one of said likelihoods oftransition is representative of a possible turn-back transition at saidjunction in which said ingress edge further represents one of saidplurality of egress edges.
 25. The apparatus of claim 17, and furthercomprising: means for determining at least one of said possibleorigination paths in said encoded routability graph.
 26. The apparatusof claim 16, wherein at least one of said likelihoods of transitioncomprises an a priori direction probability value.
 27. The apparatus ofclaim 16, and further comprising: means for providing at least saidlikelihoods of transition to at least one mobile device.
 28. Theapparatus of claim 16, wherein said computing platform is providedwithin a mobile device.
 29. A computing platform comprising: memory; andone or more processing units to: obtain, from said memory, an encodedroutability graph representative of feasible paths in an indoorenvironment represented by an encoded map; and assign likelihoods oftransition from an ingress edge in said encoded routability graph toindividual egress edges through a junction connecting said ingress edgeto a plurality of egress edges based, at least in part, on one or morefeatures of said encoded map.
 30. The computing platform of claim 29,said one or more processing units to further: assign said likelihoods oftransition based, at least in part, on possible origination paths insaid encoded routability graph connected to said junction through saidingress edge and possible destination paths connected to said junctionthrough said individual egress edges.
 31. The computing platform ofclaim 30, said one or more processing units to further: determine saidlikelihoods of transition based, at least in part, on a ratio of anumber of said possible destination paths through said individual egressedges leading away from said junction to a number of said possibleorigination paths through said ingress edge leading toward saidjunction.
 32. The computing platform of claim 30, wherein said possibleorigination paths and said possible destination paths are based, atleast in part, on point-to-point connections between nodes in a reducedversion of said encoded routability graph.
 33. The computing platform ofclaim 32, wherein at least one of said nodes in said reduced version ofsaid encoded routability graph is assigned a weight value that is based,at least in part, on a number of an encoded connectivity graph nodesrepresented by said at least one of said nodes.
 34. The computingplatform of claim 30, wherein said possible origination paths and saidpossible destination paths are based, at least in part, onpoint-to-point connections between nodes representing points of interestin said encoded map along said encoded routability graph.
 35. Thecomputing platform of claim 34, wherein at least one of said nodesrepresenting at least one of said points of interest is assigned aweight value relative to at least one other node representing at leastanother one of said points of interest.
 36. The computing platform ofclaim 30, said one or more processing units to further: affect at leastone weight value assigned to at least one node in said encodedroutability graph corresponding to said junction.
 37. The computingplatform of claim 30, wherein at least one of said likelihoods oftransition is representative of a possible turn-back transition at saidjunction in which said ingress edge further represents one of saidplurality of egress edges.
 38. The computing platform of claim 37,wherein said at least one of said likelihoods of transition that isrepresentative of said possible turn-back transition comprises apredetermined value.
 39. The computing platform of claim 30, said one ormore processing units to further: determine at least one of saidpossible origination paths in said encoded routability graph using anall-pairs shortest path (APSP) algorithm.
 40. The computing platform ofclaim 29, wherein said likelihoods of transition comprise a prioridirection probability values.
 41. The computing platform of claim 29,and further comprising: a network interface; and said one or moreprocessing units to further: initiate transmission of at least one ofsaid likelihoods of transition to at least one mobile device.
 42. Thecomputing platform of claim 29, wherein said computing platform isprovided within a mobile device.
 43. An article for use in a computingplatform, the article comprising: a non-transitory computer-readablemedium having stored therein computer implementable instructionsexecutable by one or more processing units to: obtain an encodedroutability graph representative of feasible paths in an indoorenvironment represented by an encoded map; and assign likelihoods oftransition from an ingress edge in said encoded routability graph toindividual egress edges through a junction connecting said ingress edgeto a plurality of egress edges based, at least in part, on one or morefeatures of said encoded map.
 44. The article of claim 43, said computerimplementable instructions being further executable by said one or moreprocessing units to: assign said likelihoods of transition based, atleast in part, on possible origination paths in said encoded routabilitygraph connected to said junction through said ingress edge and possibledestination paths connected to said junction through said individualegress edges.
 45. The article of claim 44, said computer implementableinstructions being further executable by said one or more processingunits to: determine said likelihoods of transition based, at least inpart, on a ratio of a number of said possible destination paths throughsaid individual egress edges leading away from said junction to a numberof said possible origination paths through said ingress edge leadingtoward said junction.
 46. The article of claim 44, wherein said possibleorigination paths and said possible destination paths are based, atleast in part, on point-to-point connections between nodes in a reducedversion of said encoded routability graph.
 47. The article of claim 46,wherein at least one of said nodes in said reduced version of saidencoded routability graph is assigned a weight value that is based, atleast in part, on a number of an encoded connectivity graph nodesrepresented by said at least one of said nodes.
 48. The article of claim44, wherein said possible origination paths and said possibledestination paths are based, at least in part, on point-to-pointconnections between nodes representing points of interest in saidencoded map along said encoded routability graph.
 49. The article ofclaim 48, wherein at least one of said nodes representing at least oneof said points of interest is assigned a weight value relative to atleast one other node representing at least another one of said points ofinterest.
 50. The article of claim 44, said computer implementableinstructions being further executable by said one or more processingunits to: affect at least one weight value assigned to at least one nodein said encoded routability graph corresponding to said junction. 51.The article of claim 44, wherein at least one of said likelihoods oftransition is representative of a possible turn-back transition at saidjunction in which said ingress edge further represents one of saidplurality of egress edges.
 52. The article of claim 51, wherein said atleast one of said likelihoods of transition that is representative ofsaid possible turn-back transition comprises a predetermined value. 53.The article of claim 44, said computer implementable instructions beingfurther executable by said one or more processing units to: determine atleast one of said possible origination paths in said encoded routabilitygraph using an all-pairs shortest path (APSP) algorithm.
 54. The articleof claim 43, wherein at least one of said likelihoods of transitioncomprises an a priori direction probability value.
 55. The article ofclaim 43, said computer implementable instructions being furtherexecutable by said one or more processing units to: initiatetransmission of at least said likelihoods of transition to at least onemobile device.
 56. The article of claim 43, wherein said computingplatform is provided within a mobile device.